---
title: "ggplotly geoms"
author: "Carson Sievert"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    css: style.css
---

```{r setup, include=FALSE}
library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)

# Make some noisily increasing data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))
```

# geom_point {data-navmenu="PRIMERO"}


Row
-----------------------------------------------------------------------

### Scatter Chart with geom_point 

```{r}
dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1)      # Use hollow circles
ggplotly(p)
```


### geom_smooth Linear Regression

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth(method=lm)   # Add linear regression line
ggplotly(p)
```

Row
-----------------------------------------------------------------------

### geom_smooth with Loess Smoothed Fit

```{r}
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
            geom_point(shape=1) +    # Use hollow circles
            geom_smooth()            # Add a loess smoothed fit curve with confidence region
ggplotly(p,dynamicTicks=TRUE)
```

### Constraining Slope with stat_smooth

#### **HEELOOO**

```{r}
ggplotly(p)
```

Row {data-height=200}
-----------------------------------------------------------------------

Sequences identified during the sequencing process are grouped in batches of identical sequences so that the number of times that sequences was found can be recorded.  This the abundance of the sequence.  The groups of identical sequences are called Operational Taxonomic Units 
(OTUs) or Amplicon Sequence Variants (ASVs), and they often (but not always) correspond to species or genera of bacteria.  In the plot below we can observe the prevalence of OTUs across all samples (the fixed threshold for an OTU to be considered present in a sample is 0.1% relative abundance). The sequencing process identified more than 1300 unique sequences, and as expected, only a few OTUs are shared by the majority of samples.  Most are rare taxa detected in only a small portion of samples. It is common to find a small number of OTUs which are dominant in the community, while most others are much less abundant and they are unlikely to be biologically related to any performance or treatment-based effects.


# otro {data-navmenu="PRIMERO"}


# Chart A {data-navmenu="towns"}


# Chart B {data-navmenu="towns"}

```{r sds}
```

# Chart C {data-navmenu="towns"}

```{r as}
```